The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 11, 2025

Filed:

Jan. 20, 2022
Applicant:

Aramco Services Company, Houston, TX (US);

Inventors:

Chicheng Xu, Houston, TX (US);

Weichang Li, Katy, TX (US);

Yaser Alzayer, Dhahran, SA;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/764 (2022.01); E21B 47/002 (2012.01); G01V 3/02 (2006.01); G01V 3/14 (2006.01); G06N 3/08 (2023.01); G06V 10/50 (2022.01); G06V 10/56 (2022.01); G06V 10/762 (2022.01); G06V 10/771 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); E21B 47/002 (2020.05); G01V 3/02 (2013.01); G01V 3/14 (2013.01); G06N 3/08 (2013.01); G06V 10/507 (2022.01); G06V 10/56 (2022.01); G06V 10/763 (2022.01); G06V 10/771 (2022.01); G06V 10/82 (2022.01); E21B 2200/22 (2020.05);
Abstract

A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.


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